A Multiresolution Details Enhanced Attentive Dual-UNet for Hyperspectral and Multispectral Image Fusion

Abstract

The fusion-based super-resolution of hyperspectral images (HSI) draws more and more attention in order to surpass the hardware constraints intrinsic to hyperspectral imaging systems in terms of spatial resolution. Low-resolution (LR) HSI is combined with a high-resolution multispectral image (HR-MSI) to achieve HR-HSI. In this paper, we propose multi-resolution details enhanced attentive dual-UNet (MDA-DUNet) to improve the spatial resolution of HSI. The entire network contains two branches. The first branch is the wavelet detail extraction module, which performs discrete wavelet transform on MSI to extract spatial detail features and then passes through the encodingdecoding. Its main purpose is to extract the spatial features of MSI at different scales. The latter branch is the spatio-spectral fusion module, which aims to inject the detail features of the wavelet detail extraction network into the HSI to reconstruct the HSI better. Moreover, this network uses an asymmetric feature selective attention model to focus on important features at different scales. Extensive experimental results on both simulated and real data show that the proposed network architecture achieves the best performance compared with several leading HSI super-resolution methods in terms of qualitative and quantitative aspects.